A review on chloride induced corrosion in reinforced concrete structures: lab and investigation.

Reinforced concrete (RC) constructions are seriously threatened by chloride-induced corrosion (CIC) and carbonation, which can result in structural degradation, safety issues, and financial losses. Electrochemical methods and microstructural analysis tests are some of the laboratory techniques used to examine key elements of CIC, such as the impact of different variables and the efficacy of mitigation solutions. studies that make use of non-destructive testing, chloride profiling, and half-cell potential measurements offer important new insights into the long-term performance and causes of RC structure deterioration in real-world circumstances.

Solar desalination system for fresh water production performance estimation in net-zero energy consumption building: A comparative study on various machine learning models.

This study employs diverse machine learning models, including classic artificial neural network (ANN), hybrid ANN models, and the imperialist competitive algorithm and emotional artificial neural network (EANN), to predict crucial parameters such as fresh water production and vapor temperatures. Evaluation metrics reveal the integrated ANN-ICA model outperforms the classic ANN, achieving a remarkable 20% reduction in mean squared error (MSE). The emotional artificial neural network (EANN) demonstrates superior accuracy, attaining an impressive 99% coefficient of determination () in predicting freshwater production and vapor temperatures.

Estimation of compressive strength of waste concrete utilizing fly ash/slag in concrete with interpretable approaches: optimization and graphical user interface (GUI).

Geo-polymer concrete has a significant influence on the environmental condition and thus its use in the civil industry leads to a decrease in carbon dioxide (CO) emission. However, problems lie with its mixed design and casting in the field. This study utilizes supervised artificial-based machine learning algorithms (MLAs) to anticipate the mechanical characteristic of fly ash/slag-based geopolymer concrete (FASBGPC) by utilizing AdaBoost and Bagging on MLPNN to make an ensemble model with 156 data points.

Bibliometric analysis and literature review of occupant thermal comfort in naturally ventilated buildings (1995-2021).

This paper presents the global research landscape and scientific progress on occupant thermal comfort in naturally ventilated buildings (OTC-NVB). Despite the growing interest in the area, comprehensive papers on the current status and future developments on the topic are currently lacking. Hence, the publication trends, bibliometric analysis, and systematic literature review of the published documents on OTC-NVB were examined.

Run-off modelling of pervious and impervious areas using couple SWAT and a novel machine learning model in cross-rivers state Nigeria.

Using the soil and water assessment tool (SWAT), runoff in pervious and impervious urban areas was simulated in this study. In the meantime, as a novel application of machine learning, the emotional artificial neural network (EANN) model was employed to enhance the SWAT obtained for this study. As a result of the EANN model's capabilities in rainfall-runoff phenomena, the SWAT-EANN couple model has been used to assess urban flooding.

Ground water quality evaluation using hydrogeochemical characterization and novel machine learning in the Chikun Local Government Area of Kaduna State, Nigeria.

The investigation collected 50 random water samples from wells and bore holes in the five wards. In the meantime, the Water Quality Index (WQI) in this region was assessed using a novel machine learning model. In this sphere of science, the Emotional Artificial Neural Network (EANN) was used as an innovative technique.